flashtext
TypeChat
flashtext | TypeChat | |
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8 | 12 | |
5,535 | 7,858 | |
- | 2.4% | |
0.0 | 9.1 | |
6 months ago | 2 days ago | |
Python | TypeScript | |
MIT License | MIT License |
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flashtext
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Show HN: LLMs can generate valid JSON 100% of the time
I have some other comment on this thread where I point out why I don’t think it’s superficial. Would love to get your feedback on that if you feel like spending more time on this thread.
But it’s not obscure? FlashText was a somewhat popular paper at the time (2017) with a popular repo (https://github.com/vi3k6i5/flashtext). Their paper was pretty derivative of Aho-Corasick, which they cited. If you think they genuinely fucked up, leave an issue on their repo (I’m, maybe to your surprise lol, not the author).
Anyway, I’m not a fan of the whatabboutery here. I don’t think OG’s paper is up to snuff on its lit review - do you?
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[P] what is the most efficient way to pattern matching word-to-word?
The library flashtext basically creates these tries based on keywords you give it.
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What is the most efficient way to find substrings in strings?
Seems like https://github.com/vi3k6i5/flashtext would be better suited here.
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[P] Library for end-to-end neural search pipelines
I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.
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How can I speed up thousands of re.subs()?
For the text part not requiring regex, https://github.com/vi3k6i5/flashtext might help
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My first NLP pipeline using SpaCy: detect news headlines with company acquisitions
Spacy for parsing the Headlines, remove stop words etc. might be ok but I think the problem is quite narrow so a set of fixed regex searches might work quite well. If regex is too slow, try: https://github.com/vi3k6i5/flashtext
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What tech do I need to learn to programmatically parse ingredients from a recipe?
I would probably use something like [flashtext](https://github.com/vi3k6i5/flashtext) which should not be too hard to port to kotlin.
- Quickest way to check that 14000 strings arent in An original string.
TypeChat
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Fuck You, Show Me the Prompt
Not sure it's related to function calling. GPT4 can do function calling without using the specific function-calling API just by injecting the schema you want into the prompt with directions and asking it to return JSON. It works like >99% of the time. Same with 3.5-turbo.
The problem is these libraries convert pydantic models into json schemas and inject them into the prompt, which uses up like 80% more tokens than just describing the schema using typescript type syntax for example. See https://microsoft.github.io/TypeChat/, where they prompt using typescript type descriptions to get json data from LLMs. It's similar to what we built but with more boilerplate.
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Semantic Kernel
Semantic Memory (renamed to Kernel Memory - https://github.com/microsoft/kernel-memory) complements SK. Guidance's features are being absorbed into SK, following the departure of that team from Microsoft. Additionally, we have TypeChat (https://github.com/microsoft/TypeChat), which aims to ensure type-safe responses from LLMs. Most features of Autogen are also being integrated into SK, along with Assistants. SK serves as the orchestration engine powering Microsoft Copilots.
- Good LLM Validation Is Just Good Validation
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Show HN: Symphony – Make functions invokable by GPT-4
I tried TypeChat for my use case and ended up defining functions as typescript data types. This approach sounds much better, and leverages the newer OpenAI function calling, which should be more reliable I would think. Thanks for creating+sharing.
https://microsoft.github.io/TypeChat/
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Show HN: LLMs can generate valid JSON 100% of the time
That re-prompting error on is what this new Microsoft library does, too: https://github.com/microsoft/TypeChat
Here's their prompt for that: https://github.com/microsoft/TypeChat/blob/c45460f4030938da3...
I think the approach using grammars (seen here, but also in things like https://github.com/ggerganov/llama.cpp/pull/1773 ) is a much more elegant solution.
- TypeChat replaces prompt engineering with schema engineering
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Introducing TypeChat from Microsoft
I'm very surprised that they're not using `guidance` [0] here.
It not only would allow them to suggest that required fields be completed (avoiding the need for validation [1]) and probably save them GPU time in the end.
There must be a reason and I'm dying to know what it is! :)
[0] https://github.com/microsoft/guidance
[1] https://github.com/microsoft/TypeChat/blob/main/src/typechat...
What are some alternatives?
KeyBERT - Minimal keyword extraction with BERT
guidance - A guidance language for controlling large language models.
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
magnitude - A fast, efficient universal vector embedding utility package.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
outlines - Structured Text Generation
yake - Single-document unsupervised keyword extraction
ai-agents-laravel - Build AI Agents for popular LLMs quick and easy in Laravel
gensim - Topic Modelling for Humans
ts-patch - Augment the TypeScript compiler to support extended functionality